User-centric Cell Clustering And Resource Allocation In UDN

The rapid growth of various forms of data traffic has become the main driving for the continued development of the communications industry.The popularity of smart terminals and the emergence of new applications,including virtual reality,online games,driverless cars,wearables,and cloud computing,requires higher quality of service.In order to cope with the surge in data and the needs of various applications,and to provide higher quality of service for the majority of end users,5G mobile network architectures and technologies have emerged at the historic moment,and ultra-dense network(UDN)technology is considered to be one of the main ways to challenge data traffic.It increases the spectrum reuse factor through dense deployment,which can increase the capacity of hot spots by more than 100 times.Due to the dense deployment,short distance,and irregular deployment of UDN,the inter-cell interference problem will be further complicated.For users,with the dense deployment of base stations,the probability that the number of base stations with close receiving power exceeds three increases sharply,users in the network are likely to receive more than two strong interferences.Therefore,inter-cell interference management technology is a crucial issue in UDN.The first part of the work was to consider the scenario where the density of users did not exceed the density of base stations in a certain coverage area.To overcome the shortcomings of the original clustering algorithm,a pre-clustering algorithm which was combined with the prediction of the user’s location at the next scheduling time was designed to reduce the average interference of mobile users,increase user throughput,and reduce user interruption rate.Through simulation,the influence of the weight of the predicted value on the performance of the system was also studied when the cooperative base stations were selected for pre-clustering.Simulation results show that the algorithm in this paper improves the average throughput and the average spectral efficiency of users in the system.The second part of this paper was to optimize the resource and energy consumption of base stations on the basis of the first part.A priority-based resource allocation algorithm was designed.The resource allocation problem was divided into three steps,namely,allocation-sleep-re-allocation process.The algorithm uses the local optimization method to approximate the global optimal solution and obtains a sub-optimal solution for resource allocation,which greatly reduces the energy consumption of the base station on the premise of ensuring user service quality.